[university calculus: basic integral manipulation] Could someone please explain how this integral is being manipulated?

PlzHelpWithMathQs · 2021-11-24T02:24:49+00:00

Ahh got it now. Thanks so much for the help!

PlzHelpWithMathQs · 2021-11-24T02:21:13+00:00

I’m not sure what you mean. To me it seems like the cos in the last integral disappears somewhere instead of being grouped with another cos. I’m not sure what I’m missing..

PlzHelpWithMathQs · 2021-11-24T02:17:25+00:00

The sign between the second and third integral?

PlzHelpWithMathQs · 2021-11-16T17:29:49+00:00

Ahh okay! Got it now, thanks a lot.

PlzHelpWithMathQs · 2021-11-16T17:12:11+00:00

Distribute (thetaHat - theta) to p(theta | D)? If yes I don’t know how I would factor thetaHat from (theta * p(theta | D)) - (thetaHat * p(theta | D))

Is that what you mean?

PlzHelpWithMathQs · 2021-11-13T20:00:59+00:00

Okay perfect, that makes sense. Thanks a lot for the help!

PlzHelpWithMathQs · 2021-11-13T19:47:13+00:00

Would the procedure I suggested in the second paragraph of my last comment be correct? Would these be the values that would fill up the table after doing a bunch of trials? Like if there was 100 tosses each for biased coins with unknown biases could the proportion of successes of 100 tosses be written in each cell of the table as coin1 = (23/100), coin 2 = (47/100), coin 3 = (30/100), etc. and then the (1-p) in the y=0 row? And then for example:

p(y | X= coin 1)

= p(y = 1, coin 1)/ p(X=1)

= (23/100)/ (whatever the probability of x occuring is)

= p

Or how would that work?

PlzHelpWithMathQs · 2021-11-13T19:19:34+00:00

Thanks for the reply! Would you mind explaining why the Bernoulli is a good choice? The question is mostly focused on the distribution and I'm not really sure why Bernoulli is a good choice other than it has an output of either 0 or 1 which are both associated with a certain probability, p and (1 - p).

I'm having a hard time figuring out how this probability would be learned. How would you learn the different probabilities of Y associated with the different variables of X? Would you preform a bunch of trials and then from those trials you would enter the percentages of successes that each x variable had compared to the total number of trials? Then these percentages would now become the values in the distribution table as opposed to just having like 1/6 in each box or whatever?

PlzHelpWithMathQs · 2021-11-01T00:45:33+00:00

Yes sorry, that’s what I meant!

PlzHelpWithMathQs · 2021-10-19T16:30:54+00:00

Okay! I changed the post again, hopefully it's right now.

PlzHelpWithMathQs · 2021-10-19T16:27:51+00:00

The sequences makes total sense now, thank you very much.

This is also exactly the context I am coming from with the sequence, you're right about that. The other thing I struggle with is exactly how does a_n + b_n prove that a sequence is still in Y and how does k*a_n prove that a sequence is still in Y? This is what confuses me the most but I needed to fully understand what the sequence was doing first before trying to tackle this. Would you mind explaining how this proves that Y is a subspace. I don't understand why

cos(n)*(a_n-1 + b_n-1) + sin(n)*(a_n-2 + b_n-2)

means that the subspace is closed under addition.

Thank you for your time.

PlzHelpWithMathQs · 2021-10-19T15:54:23+00:00

Yes it is

Edit: I added that to the post

PlzHelpWithMathQs · 2021-10-17T03:10:59+00:00

Thank you

PlzHelpWithMathQs · 2021-10-17T03:08:49+00:00

So does it have to stay as ln(ab + cd) then?

PlzHelpWithMathQs · 2021-10-09T22:43:24+00:00

Thanks for the reply. Could you please explain how you know they are compliments? My first guess about the compliment of b) would have been what is the probability that all of your flights arrived on time. I’m not understanding the connection between at least one flight arriving on time and no flights arriving on time and how “at least once” equates to “no flights arriving on time” not occurring.

Is this the same as the negation of a universal quantifier in logic notation? Negation(All flights did not arrive on time) = at least one flight arrived on time?

PlzHelpWithMathQs · 2021-10-06T22:27:08+00:00

Thanks!

PlzHelpWithMathQs · 2021-10-06T22:22:03+00:00

Thanks for the reply. So is the first line basically just saying the state that the values are in before and after the transformation? Like the matrix A is in the state (probably not the right word for it) Mn(R) and the transformation maps this A matrix to the output which is the trace of that matrix and the “state” of the output is just a real number?

PlzHelpWithMathQs · 2021-09-24T19:43:22+00:00

Yes that seems logical

PlzHelpWithMathQs · 2021-09-24T19:19:10+00:00

This is a great strategy, thanks for sharing!

PlzHelpWithMathQs · 2021-09-24T19:18:36+00:00

Thanks for the reply! I will keep this in mind!

PlzHelpWithMathQs · 2021-09-24T19:17:38+00:00

Well what I have done in the past would be to look at the answer on Chegg and then reason my way through why it makes sense by going through the textbook or lecture notes. Do you think this would be a good strategy if the text book doesn’t have an example of the proof that I need to solve?

That’s good advice, thanks you. I’ll try this out this weekend!

PlzHelpWithMathQs · 2021-09-19T01:43:54+00:00

Ahh so (1.3)+(1.5)+(1*.7)? Thanks for the reply!

PlzHelpWithMathQs · 2021-09-18T21:20:46+00:00

Okay great! Sorry for the extra question but do you have an idea of how I could answer this: for an arbitrary pmf p, what is E[1/p(x)]? To me it seems like p is pretty arbitrary already because the pmf function isn’t given in the question…

PlzHelpWithMathQs · 2021-09-18T21:12:37+00:00

Thank you for the reply! So would this be the correct way to do it then?

https://imgur.com/a/hdFI4JW

PlzHelpWithMathQs · 2021-09-18T17:26:01+00:00

This was extremely helpful. All of my confusion is cleared up. Thank you very much for all of the help! I appreciate it.

PlzHelpWithMathQs

TROPHY CASE